potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 15:23:05 +04:00
Code Packages Releases Wiki Activity

ml: apply CV_OVERRIDE/CV_FINAL

Browse Source
This commit is contained in:
Alexander Alekhin
2018-03-15 16:16:58 +03:00
parent 3314966acb
commit 4d0dd3e509
13 changed files with 370 additions and 301 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/ml/src/lr.cpp
+24 -18
View File
@@ -81,30 +81,36 @@ public:
TermCriteria term_crit;
};
class LogisticRegressionImpl : public LogisticRegression
class LogisticRegressionImpl CV_FINAL : public LogisticRegression
{
public:
LogisticRegressionImpl() { }
virtual ~LogisticRegressionImpl() {}
CV_IMPL_PROPERTY(double, LearningRate, params.alpha)
CV_IMPL_PROPERTY(int, Iterations, params.num_iters)
CV_IMPL_PROPERTY(int, Regularization, params.norm)
CV_IMPL_PROPERTY(int, TrainMethod, params.train_method)
CV_IMPL_PROPERTY(int, MiniBatchSize, params.mini_batch_size)
CV_IMPL_PROPERTY(TermCriteria, TermCriteria, params.term_crit)
inline double getLearningRate() const CV_OVERRIDE { return params.alpha; }
inline void setLearningRate(double val) CV_OVERRIDE { params.alpha = val; }
inline int getIterations() const CV_OVERRIDE { return params.num_iters; }
inline void setIterations(int val) CV_OVERRIDE { params.num_iters = val; }
inline int getRegularization() const CV_OVERRIDE { return params.norm; }
inline void setRegularization(int val) CV_OVERRIDE { params.norm = val; }
inline int getTrainMethod() const CV_OVERRIDE { return params.train_method; }
inline void setTrainMethod(int val) CV_OVERRIDE { params.train_method = val; }
inline int getMiniBatchSize() const CV_OVERRIDE { return params.mini_batch_size; }
inline void setMiniBatchSize(int val) CV_OVERRIDE { params.mini_batch_size = val; }
inline TermCriteria getTermCriteria() const CV_OVERRIDE { return params.term_crit; }
inline void setTermCriteria(TermCriteria val) CV_OVERRIDE { params.term_crit = val; }
virtual bool train( const Ptr<TrainData>& trainData, int=0 );
virtual float predict(InputArray samples, OutputArray results, int flags=0) const;
virtual void clear();
virtual void write(FileStorage& fs) const;
virtual void read(const FileNode& fn);
virtual Mat get_learnt_thetas() const { return learnt_thetas; }
virtual int getVarCount() const { return learnt_thetas.cols; }
virtual bool isTrained() const { return !learnt_thetas.empty(); }
virtual bool isClassifier() const { return true; }
virtual String getDefaultName() const { return "opencv_ml_lr"; }
virtual bool train( const Ptr<TrainData>& trainData, int=0 ) CV_OVERRIDE;
virtual float predict(InputArray samples, OutputArray results, int flags=0) const CV_OVERRIDE;
virtual void clear() CV_OVERRIDE;
virtual void write(FileStorage& fs) const CV_OVERRIDE;
virtual void read(const FileNode& fn) CV_OVERRIDE;
virtual Mat get_learnt_thetas() const CV_OVERRIDE { return learnt_thetas; }
virtual int getVarCount() const CV_OVERRIDE { return learnt_thetas.cols; }
virtual bool isTrained() const CV_OVERRIDE { return !learnt_thetas.empty(); }
virtual bool isClassifier() const CV_OVERRIDE { return true; }
virtual String getDefaultName() const CV_OVERRIDE { return "opencv_ml_lr"; }
protected:
Mat calc_sigmoid(const Mat& data) const;
double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
@@ -392,7 +398,7 @@ struct LogisticRegressionImpl_ComputeDradient_Impl : ParallelLoopBody
}
void operator()(const cv::Range& r) const
void operator()(const cv::Range& r) const CV_OVERRIDE
{
const Mat& _data = *data;
const Mat &_theta = *theta;